MAT 1455 Introduction to Data Science
An introductory course in data science for students interested in information technology, computer science, and related fields. Topics include curation of data; enhanced data visualization; statistical models, estimation, and prediction; and applications of data science.
Prerequisites: MAT 0300 and Other (with a grade of "C" or better)
- Define machine learning and statistical learning technique, classify data using machine learning techniques, differentiate between supervised and unsupervised learning technique.
- Differentiate between settings where methods of analysis of data will lead to biased results and to unbiased results, show bias in its various forms.
- Distinguish between types of data, acquire raw data, organize, standardize, and clean this raw data.
- Classify and summarize data using appropriate charting techniques based on the type of data, identify common distribution models and select the model that fits the data, predict a time series plots for the data.
- Use probability distributions to approximate probabilities, estimate parameters, and make predictions.
Credit Hours: 5
- Classroom: 5 hours
- Division: Science, Mathematics and Engineering
- Department: Mathematics
- Repeatable Credit: No
- Offered Online: No
Not currently offered this term